1. Field of the Invention
The present invention relates to a navigation system, and more particularly to an apparatus and method for detecting a location of a movable body such as a car in navigation system.
2. Description of the Related Art
Typically, a car navigation system provides a driver with current location information of the car and an optimum route to destination and guides the driver according to a traveling route. The most basic function of the car navigation system is to accurately determine a current location of the car.
FIG. 1 is a schematic constructional view of a conventional navigation system, which mainly illustrates a construction necessary for measuring a current location of a car in a car navigation system. Referring to FIG. 1, a typical car navigation system includes a GPS sensor 10, a dead reckoning (DR) sensor 20, a map data storage 30, a current location detector 40 and a display 50.
The map data storage 30 stores a digital map. FIGS. 2a and 2b illustrate a conventional method of storing map data. Typically, for rapid search in map data, a total map is divided into a plurality of parts, known as “map sections”, of a predetermined unit size, and road information is displayed by means of nodes and links in each of the map sections. FIG. 2a shows a map of South Korea, which is divided into 12 map sections, and FIG. 2b shows one of the 12 map sections, in which road information is displayed with nodes and links.
The GPS sensor 10 is a sensor for receiving a Global Positioning System (GPS) signal. In the present example, GPS implies a system for tracing a global location by means of 24 artificial satellites orbiting at a height of about 20,183 km. That is, GPS is a satellite navigation system in which a GPS receiver installed on an observational station receives the radio wave transmitted from a satellite, the accurate location of which is known, so that the time necessary in receiving a radio wave is calculated, thereby obtaining a location of the observational station. The GPS sensor 10 receives the GPS signal and transmits location information using geometric coordinates x, y, z, and current time information t of a car to the current location detector 40.
The DR sensor 20 is a sensor detecting its own relative location and moving direction by means of previous location information. Typically, the DR sensor may be divided into a sensor for measuring a distance traversed, for instance, speedometer, odometer, accelerometer, etc., and a sensor for measuring an angle of rotation, for instance, geomagnetic sensor, gyro, etc. Accordingly, the DR sensor 20 senses a velocity v and a moving direction θ of a car and transmits it to the current location detector 40.
The current location detector 40 extracts map data of a corresponding region, on the basis of location information of the car transmitted from the GPS sensor 10 and the DR sensor 20 and performs map-matching for location information of the car using map data. That is, the current location detector 40 indicates a location of a user on previously constructed map data, known as a “digital map”, and confirms the location of the car with one point on the map. Further, as a result of the confirmation, the current location detector 40 computes location information of the car and displays the location information through a display 50.
In the present example, typically, since the GPS sensor 10 and the DR sensor 20 include an error in the measured value, the current location detector 40 performs map-matching to correct the error. For instance, the GPS sensor 10 may have errors such as ionization layer delay error, satellite clock error, multi-path and the DR sensor 20 may have an initial alignment error and a conversion factor error. Particularly, when the car passes high buildings, trees, tunnels, etc., the car may not sufficiently receive the GPS signal and thus the error becomes larger. Also, when the error is accumulated, it is difficult for the current location detector 40 to accurately determine the location of the car. When location information of the car measured using sensors, which have errors as described above, is indicated on a map, the location information does not agree with the actual location of the car. Accordingly, in order to correct errors, a typical current location detector 40 performs map-matching using a digital map.
FIG. 3 illustrates the current location detector 40, typically the current location detector 40 includes a sensor 41, a map data detector 42, a memory 43, a filter 44 and a map-matching unit 45. The sensor 41 receives sensor data, for example, x, y, z, t, v and θ, from the GPS sensor 10 and the DR sensor 20, and transmits the car location coordinate information x, y for detecting a map data from among the sensor data x, y, z, t, v and θ to the map data detector 42. The map data detector 42 extracts map data of a corresponding region from the map data storage 30 on the basis of the location information x, y and stores it in the memory 43.
The filter 44 receives location coordinates x, y, and time t, velocity v and angle θ of the car and computes an optimum location x′, y′ and angle θ′ using the values as measured value of the filter. The filter 44 typically uses a GPS/DR integrated Kalman filter combining GPS and DR. Since the Kalman filter mathematically minimizes a measured error of a variable and has a characteristic of being suitable for computation and prediction of the variable, it is known as a prediction filter. Furthermore, the Kalman filter can predict the optimum state even under an error circumstances. Accordingly, the Kalman filter is typically used in order to minimize a measured error of a sensor in the car navigation system.
The characteristic of the Kalman filter, values x′, y′ and θ′ computed by the filter 44 do not agree with an actual map line, owing to the error of the GPS sensor 10 and the DR sensor 20. The map-matching unit 45 performs map-matching using the values x′, y′ and θ′ computed by the filter 44 and the digital map stored in the memory 43 in order to correct the error. That is, the map-matching unit 45 corrects the computed location by matching the values x′, y′ and θ′ computed by the filter 44 on the digital map.
The filter 44 and feed back location coordinates δx, δy, velocity δ v and an angle δθ information of the car outputted from the filter 44, in order to correct the error of the DR sensor 20 on the basis of GPS data, providing a relatively accurate location information in comparison with the DR sensor 20. Further, the filter 44 receives the location/angle of a car matched from the map-matching unit 45 and a difference in location and angle, that is, Δx, Δy and Δδ computed by the filter 44, and corrects the GPS/DR integrated Kalman filter in the filter 44.
A conventional method for detecting a current location of a car in a car navigation system is described with reference to FIG. 4a. When the location coordinate information x, y, z and time t of the car received by the GPS sensor and the velocity v and direction information θ of the car sensed by the DR sensor 20 (FIG. 1) are inputted in step S10, the car navigation system detects a region corresponding to the sensor data x, y, z, t, v and θ from the pre-stored digital map, as discussed with reference to FIGS. 2a and 2b, and stores it separately in memory 43 (FIG. 3 in step S20. In step S30 the car navigation system initializes the GPS/DR integrated Kalman filter model for computing the location information of the car using the sensor data, then the car navigation system computes the location information of the car using the sensor data as measured value of the GPS/DR integrated Kalman filter in step S40.
Typically the GPS sensor 10 (FIG. 1) and the DR sensor 20 (FIG. 1) include an error within the measured value, the location computed by the GPS/DR integrated Kalman filter model is not accurate owing to the error included in the GPS sensor 10 (FIG. 1) and the DR sensor 20 (FIG. 1). In order to solve this problem, the GPS sensor and the DR sensor correct the error of GPS sensor and the DR sensor by feedback data of the measured data in step S50. In this case, the data being fed back to the GPS/DR integrated Kalman filter 44 (FIG. 3) is the location coordinates δx, δy, velocity δv and angle δθ information of the car outputted from the GPS/DR integrated Kalman filter.
Further, in step S60, the location information computed in step S40 is map-matched using the digital map about region corresponding to the sensor data stored in step S20. In step S70, the GPS/DR integrated Kalman filter is corrected using the map-matching result. That is, the GPS/DR integrated Kalman filter is corrected using filter correction data Δx, Δy and Δθ generated by the map-matching result.
FIG. 4b illustrates a process flow of the map-matching step S60 (FIG. 4a). In step S61, link information and node information are detected from the map, which is an object of map-matching, that is, a digital map stored in step S20 (FIG. 4a). In step S62, it is judged whether the location computed in step S40 (FIG. 4a) on the basis of the link information and node information is a crossroads. In judging whether the computed location is a crossroads, an existence of a crossroad within a predetermined distance may be detected using a forward node information and link information. It can be determined that the computed location is a crossroads, when a trace of the car calculated from the GPS/DR integrated filter passes a node of the crossroads or when a measured attitude angle has a large difference from a direction angle of the current computed road, without regard to that the trace has not passed yet, or has already passed.
From the judgment in step S62, if the location computed in step S40 (FIG. 4a) is a crossroads, a link of an adjacent crossroads is selected in step S63. If not, a link of the shortest distance from the computed location is selected in step S64. In this case, the selected link becomes matched map information for the computed location information.
As described above, when the map-matching has been performed about the computed location information, x and y coordinate corresponding to the matched link are calculated in step S65. In step S66, a difference between the x and the y coordinate included in the computed location information and the x and the y coordinates calculated in step S65 are outputted as filter correction data. The filter correction data is a difference, Δx, Δy and Δθ, between a location/angle of the car matched in step S63, S64 and a location/angle computed in the filter 44. The error of the GPS/DR integrated Kalman filter is corrected using the outputted filter correction data in step S70 (FIG. 4a).
FIG. 5 shows an example of a method for correcting a location of a car by conventional method in a car navigation system, or in other words, correcting location information computed through the GPS/DR integrated filter by means of map-matching. Circles represent location information computed by the GPS/DR integrated filter and straight lines represent the matched location information.
First, a forced correction of the attitude angle and location of the car is implemented for general traveling. For instance, when it is judged that a car is traveling along a bridge, a tunnel, or a long, straight road, the attitude angle Δθ of the car is adjusted, since it can be said that the attitude angle of the map-matched car is corrected. As shown in FIG. 5a, since an error of ‘Δy’ is generated when the car is traveling on a horizontal road, the value ‘y’ computed in the GPS/DR integrated filter is corrected by the difference ‘Δy’. Further, as shown in FIG. 5b, since an error of ‘Δx’ is generated when the car is traveling on vertical road, the value ‘x’ computed in the GPS/DR integrated filter is corrected by the difference ‘Δx’. Also, as shown in FIG. 5c, when the car passes a crossroads, the location of the car is corrected to the intersection by the difference ‘Δx and Δy’.
A conventional current location detection apparatus and method thereof using the aforementioned correction method for attitude angle, can not adjust an absolute location of a car and a forced correction method in the vertical and horizontal directions includes error and uncertainty. Further, the conventional current location detection apparatus and method thereof can perform a location correction only in limited cases, for instance, when the car continuously travels on straight line direction during a predetermined time or passes a crossroads. That is, since the conventional method can perform a sensor correction only in limited section, a real-time location correction in a car navigation system requiring constantly accurate location has been impossible. Accordingly, the current location detection apparatus and method thereof can not accurately detect a location of the car.
Further, in order to correct the error of the GPS sensor/DR sensor and compute the location information of a car, a typical centralized Kalman filter has been used in the prior art. In the present example, the centralized Kalman filter can most easily realize an optimum filter when the order of the equation used for implementing the system is low. However, when the order of the equation used for implementing the system is high, it is difficult for the centralized Kalman filter to calculate in real-time since the calculation load of an inverse matrix and covariance matrix increases. Further, when a sensor is out of order, the centralized Kalman filter can judge it to be out of order only after measured value taken from several sensors is processed. Accordingly, it is difficult for the conventional prior art to prevent an erroneous measured value from affecting a computed measured value for the location information of the car.